Gonna Be a Bright, Bright, Sunshiny Day

We met Sebastian Thrun last time. He’s a bright guy with a sunshiny disposition who’s not worried about robots and artificial intelligence taking over all the good jobs, even his own. Instead, he’s perfectly okay if technology eliminates most of what he does every day because he believes human ingenuity will fill the vacuum with something better. This is from his conversation with TED curator Chris Anderson:

“If I look at my own job as a CEO, I would say 90 percent of my work is repetitive, I don’t enjoy it, I spend about four hours per day on stupid, repetitive email. And I’m burning to have something that helps me get rid of this. Why? Because I believe all of us are insanely creative… What this will empower is to turn this creativity into action.

“We’ve unleashed this amazing creativity by de-slaving us from farming and later, of course, from factory work and have invented so many things. It’s going to be even better, in my opinion. And there’s going to be great side effects. One of the side effects will be that things like food and medical supply and education and shelter and transportation will all become much more affordable to all of us, not just the rich people.”

Anderson sums it up this way:

“So the jobs that are getting lost, in a way, even though it’s going to be painful, humans are capable of more than those jobs. This is the dream. The dream is that humans can rise to just a new level of empowerment and discovery. That’s the dream.”

Another bright guy with a sunshiny disposition is David Lee, Vice President of Innovation and the Strategic Enterprise Fund for UPS. He, too, shares the dream that technology will turn human creativity loose on a whole new kind of working world. Here’s his TED talk (click the image):

David Lee TED talk

Like Sebastian Thrun, he’s no Pollyanna:  he understands that yes, technology threatens jobs:

“There’s a lot of valid concern these days that our technology is getting so smart that we’ve put ourselves on the path to a jobless future. And I think the example of a self-driving car is actually the easiest one to see. So these are going to be fantastic for all kinds of different reasons. But did you know that ‘driver’ is actually the most common job in 29 of the 50 US states? What’s going to happen to these jobs when we’re no longer driving our cars or cooking our food or even diagnosing our own diseases?

“Well, a recent study from Forrester Research goes so far to predict that 25 million jobs might disappear over the next 10 years. To put that in perspective, that’s three times as many jobs lost in the aftermath of the financial crisis. And it’s not just blue-collar jobs that are at risk. On Wall Street and across Silicon Valley, we are seeing tremendous gains in the quality of analysis and decision-making because of machine learning. So even the smartest, highest-paid people will be affected by this change.

“What’s clear is that no matter what your job is, at least some, if not all of your work, is going to be done by a robot or software in the next few years.”

But that’s not the end of the story. Like Thrun, he believes that the rise of the robots will clear the way for unprecedented levels of human creativity — provided we move fast:

“The good news is that we have faced down and recovered two mass extinctions of jobs before. From 1870 to 1970, the percent of American workers based on farms fell by 90 percent, and then again from 1950 to 2010, the percent of Americans working in factories fell by 75 percent. The challenge we face this time, however, is one of time. We had a hundred years to move from farms to factories, and then 60 years to fully build out a service economy.

“The rate of change today suggests that we may only have 10 or 15 years to adjust, and if we don’t react fast enough, that means by the time today’s elementary-school students are college-aged, we could be living in a world that’s robotic, largely unemployed and stuck in kind of un-great depression.

“But I don’t think it has to be this way. You see, I work in innovation, and part of my job is to shape how large companies apply new technologies. Certainly some of these technologies are even specifically designed to replace human workers. But I believe that if we start taking steps right now to change the nature of work, we can not only create environments where people love coming to work but also generate the innovation that we need to replace the millions of jobs that will be lost to technology.

“I believe that the key to preventing our jobless future is to rediscover what makes us human, and to create a new generation of human-centered jobs that allow us to unlock the hidden talents and passions that we carry with us every day.”

More from David Lee next time.

If all this bright sunshiny perspective made you think of that old tune, you might treat yourself to a listen. It’s short, you’ve got time.

And for a look at a current legal challenge to the “gig economy” across the pond, check out this Economist article from earlier this week.

Learning to Learn

“I didn’t know robots had advanced so far,” a reader remarked after last week’s post about how computers are displacing knowledge workers. What changed to make that happen? The machines learned how to learn. This is from Artificial Intelligence Goes Bilingual—Without A Dictionary, Science Magazine, Nov. 28, 2017.

“Imagine that you give one person lots of Chinese books and lots of Arabic books—none of them overlapping—and the person has to learn to translate Chinese to Arabic. That seems impossible, right?” says… Mikel Artetxe, a computer scientist at the University of the Basque Country (UPV) in San Sebastiàn, Spain. “But we show that a computer can do that.”

Most machine learning—in which neural networks and other computer algorithms learn from experience—is “supervised.” A computer makes a guess, receives the right answer, and adjusts its process accordingly. That works well when teaching a computer to translate between, say, English and French, because many documents exist in both languages. It doesn’t work so well for rare languages, or for popular ones without many parallel texts.

[This learning technique is called] unsupervised machine learning. [A computer using this technique] constructs bilingual dictionaries without the aid of a human teacher telling them when their guesses are right.

Hmmm… I could have used that last year, when my wife and I spent three months visiting our daughter in South Korea. The Korean language is ridiculously complex; I never got much past “good morning.”

Alpha Go match

Go matches were a standard offering on the gym TV’s where I worked out in Seoul. (Imagine two guys in black suits staring intently at a game board — not exactly a riveting workout visual.) Like the Korean language, Go is also ridiculously complex, and mysterious, too:  the masters seem to make moves more intuitively than analytically. But the days of human Go supremacy are over. Google wizard and overall overachiever Sebastian Thrun[1] explains why in this conversation with TED Curator Chris Anderson:

sebastian thrun TED

“Artificial intelligence and machine learning is about 60 years old and has not had a great day in its past until recently. And the reason is that today, we have reached a scale of computing and datasets that was necessary to make machines smart. The new thing now is that computers can find their own rules. So instead of an expert deciphering, step by step, a rule for every contingency, what you do now is you give the computer examples and have it infer its own rules.

“A really good example is AlphaGo. Normally, in game playing, you would really write down all the rules, but in AlphaGo’s case, the system looked over a million games and was able to infer its own rules and then beat the world’s residing Go champion. That is exciting, because it relieves the software engineer of the need of being super smart, and pushes the burden towards the data.

“20 years ago the computers were as big as a cockroach brain. Now they are powerful enough to really emulate specialized human thinking. And then the computers take advantage of the fact that they can look at much more data than people can. AlphaGo looked at more than a million games.  No human expert can ever study a million games. So as a result, the computer can find rules that even people can’t find.”

Thrun made those comments in April 2017. AlphaGo’s championship reign was short-lived:  six months later it lost big to a new cyber challenger that taught itself without reviewing all that data. This is from AlphaGo Zero Shows Machines Can Become Superhuman Without Any Help, MIT Technology Review, October 18, 2017.

AlphaGo wasn’t the best Go player on the planet for very long. A new version of the masterful AI program has emerged, and it’s a monster. In a head-to-head matchup, AlphaGo Zero defeated the original program by 100 games to none.

Whereas the original AlphaGo learned by ingesting data from hundreds of thousands of games played by human experts, AlphaGo Zero started with nothing but a blank board and the rules of the game. It learned simply by playing millions of games against itself, using what it learned in each game to improve.

The new program represents a step forward in the quest to build machines that are truly intelligent. That’s because machines will need to figure out solutions to difficult problems even when there isn’t a large amount of training data to learn from.

“The most striking thing is we don’t need any human data anymore,” says Demis Hassabis, CEO and cofounder of DeepMind [the creators of AlphaGo Zero].

“By not using human data or human expertise, we’ve actually removed the constraints of human knowledge,” says David Silver, the lead researcher at DeepMind and a professor at University College London. “It’s able to create knowledge for itself from first principles.”

Did you catch that? “We’ve removed the constraints of human knowledge.” Wow. No wonder computers are elbowing all those knowledge workers out of the way.

What’s left for human to do? We’ll hear from Sebastian Thrun and others on that topic next time.

[1] Sebastian Thrun’s TED bio describes him as “an educator, entrepreneur and troublemaker. After a long life as a professor at Stanford University, Thrun resigned from tenure to join Google. At Google, he founded Google X, home to self-driving cars and many other moonshot technologies. Thrun also founded Udacity, an online university with worldwide reach, and Kitty Hawk, a ‘flying car’ company. He has authored 11 books, 400 papers, holds 3 doctorates and has won numerous awards.”